[jira] [Created] (FLINK-16327) Add TableEnvironment.fromElements interfaces for usability

[Shang Yuanchun (Jira)]

Zhenghua Gao created FLINK-16327:
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             Summary: Add TableEnvironment.fromElements interfaces for usability
                 Key: FLINK-16327
                 URL: https://issues.apache.org/jira/browse/FLINK-16327
             Project: Flink
          Issue Type: New Feature
          Components: Table SQL / API
    Affects Versions: 1.11.0
            Reporter: Zhenghua Gao


h1. Interface
{code:java}
/**
   * Creates a table from a group of objects (known as its elements). The schema of the table
   * would be inferred from the type of elements.
   *
   * @param data a group of objects.
   */
Table fromElements(Collection<?> data);
/**
   * Creates a table from a group of objects (known as its elements). The schema of the table
   * would be inferred from the passed in data type.
   *
   * @param data a group of objects
   * @param dataType the data type of the data
   */
Table fromElements(Collection<?> data, DataType dataType);
{code}
h1. Use Case
 * One potential use case for Table API

{code:java}
@Test
def testUnregisteredCollectionSource1(): Unit = {
  val env = StreamExecutionEnvironment.getExecutionEnvironment
  val tEnv = StreamTableEnvironment.create(env)
  StreamITCase.testResults = mutable.MutableList()

  val data = Seq(
    Row.of("Mike", new JInt(5), new JDouble(12.3), "Smith"))
 
  tEnv.fromElements(data.asJava)
  .as('first, 'id, 'score, 'last)
    .where('id > 4)
    .select('last, 'score * 2)
    .toAppendStream[Row]
    .addSink(new StreamITCase.StringSink[Row])

  env.execute()
}

@Test
def testUnregisteredCollectionSource2(): Unit = {
  val env = StreamExecutionEnvironment.getExecutionEnvironment
  val tEnv = StreamTableEnvironment.create(env)
  StreamITCase.testResults = mutable.MutableList()

  val data = Seq(
    Row.of("Mike", new JInt(5), new JDouble(12.3), "Smith"))

  val dataType = DataTypes.ROW(
    DataTypes.FIELD("first", DataTypes.STRING()),
    DataTypes.FIELD("id", DataTypes.INT()),
    DataTypes.FIELD("score", DataTypes.DOUBLE()),
    DataTypes.FIELD("last", DataTypes.STRING()))

  tEnv.fromElements(data.asJava, dataType)
    .where('id > 4)
    .select('last, 'score * 2)
    .toAppendStream[Row]
    .addSink(new StreamITCase.StringSink[Row])

  env.execute()
}
{code}
 * One potential use case for SQL

{code:java}
@Test
def testUnregisteredCollectionSource1(): Unit = {
  val env = StreamExecutionEnvironment.getExecutionEnvironment
  val tEnv = StreamTableEnvironment.create(env)
  StreamITCase.testResults = mutable.MutableList()

  val data = Seq(
    Row.of("Mike", new JInt(5), new JDouble(12.3), "Smith"))
 
  val table = tEnv.fromElements(data.asJava).as('first, 'id, 'score, 'last)
 
  tEnv.createTemporaryView("T", table)

  tEnv.sqlQuery("SELECT last, score * 2 FROM T WHERE id > 4")
  .toAppendStream[Row]
  .addSink(new StreamITCase.StringSink[Row])

  env.execute()
}

@Test
def testUnregisteredCollectionSource2(): Unit = {
  val env = StreamExecutionEnvironment.getExecutionEnvironment
  val tEnv = StreamTableEnvironment.create(env)
  StreamITCase.testResults = mutable.MutableList()

  val data = Seq(
    Row.of("Mike", new JInt(5), new JDouble(12.3), "Smith"))

  val dataType = DataTypes.ROW(
    DataTypes.FIELD("first", DataTypes.STRING()),
    DataTypes.FIELD("id", DataTypes.INT()),
    DataTypes.FIELD("score", DataTypes.DOUBLE()),
    DataTypes.FIELD("last", DataTypes.STRING()))

  val table = tEnv.fromElements(data.asJava, dataType)
  tEnv.createTemporaryView("T", table)

  tEnv.sqlQuery("SELECT last, score * 2 FROM T WHERE id > 4")
  .toAppendStream[Row]
  .addSink(new StreamITCase.StringSink[Row])

  env.execute()
}
{code}
h1. The proposal
 * data type inference

We need to infer the data type from the data for the first interface. A potential tool is the DataTypeExtractor, but it doesn't support scala.tuple, Row, etc. For the most popular in our test cases Row or scala.tuple type, we could enumerate and use a recursive traversal method to get all available types of underlying objects. This can solve most of the cases and improve usability.
 * proposed changes
 ** A CollectionQueryOperation which implements QueryOperation to describe the relational operation
 ** The logical and physical RelNode for legacy planner. In the physical node, we can translate the data to DataStream
 ** The logical and physical RelNode for blink planner. In the physical node, we can translate the data to Transformation

 



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